Project Description:
Prototype-based classification offers intuitive and powerful machine learning
tools, which is particularly interesting for interdisciplinary applications due to easy
interpretability of the results. Research has been conducted to exactly investigate the learning behavior
of popular heuristic learning rules in relevant model situations by means of statistical physics.

Further, extended learning rules have been developed which are based on a clear mathematical objective
and which allow a general matrix adaptation, taking relevance weighting as well as correlations into account.
Interestingly, learning theoretical generalization bounds can be derived which show that the method
can be interpreted as large margin optimization.